Session 1 - Introduction and Overview
This section provides M&A professionals with essential context on artificial intelligence and its practical applications in deal-making.
It will equip M&A professionals with a clear, concise overview of artificial intelligence (AI) and machine learning (ML). Participants will learn key definitions and terminology that demystify what AI systems are, what they can do, and where their limitations lie.
To achieve this, we provide a practical understanding of core machine learning concepts, as well as deep neural networks and large language models. The course then covers common failure modes of AI systems, mitigation strategies, and ethical and environmental considerations. This will assist participants in working more effectively with modern AI tools, knowing which tasks they are well-suited for and avoiding common pitfalls.
- What is AI & Machine Learning?
- Basic definitions and core concepts
- Understanding the AI landscape
- Key types: Supervised, Unsupervised, and Reinforcement Learning
- Large Language Models (LLMs):
- What are LLMs and how do they work?
- Training process and capabilities
- Current limitations and failure modes
- The "Attribution" problem:
- Understanding AI hallucinations
- Why LLMs sometimes generate incorrect information
- Mitigation strategies for professional use
- Practical applications;
- Current AI tools in M&A workflows
- Realistic expectations vs. hype
- Quality control considerations
- Risk Management:
- Data security and confidentiality
- Professional liability considerations
- Building verification processes
Session 2: Core Prompting Principles
Note that these prompting principles apply to a wide range of disciplines, not only M&A, but also law and corporate finance
- Universal application across AI platforms:
- Prompting principles apply to all major LLMs (ChatGPT, Claude, Gemini, etc.)
- Techniques are now particularly relevant for Microsoft Copilot (which now has access to ChatGPT-5 & Anthropic/Claude)
- The framework ensures consistency regardless of which AI platform your organisation deploys
- Where is AI being used in M&A?
- Where AI works effectively in M&A
- Where AI has limitations in M&A
- Setting up your LLM for professional use:
- Profile configuration
- Custom instructions examples
- Understanding data limitations and constraints:
- Token limitations across platforms
- Various strategic workarounds
- Professional data considerations
- Practical verification and prompting techniques:
- Hallucination filters
- Temperature filters
- SCOPE framework overview:
- The SCOPE framework: a systematic approach to prompting
- Components of the SCOPE framework:
- S = Self: your role, professional context and tone
- C = Context: deal situation & participants
- O = Objective: what decision does this support?
- P = Parameters: format, length, detail level, style, tone
- E = Execute: precise execution instructions
- Enhanced prompting techniques:
- Chain of thought methodology – use and application
- Tree of thought methodology – use and application
- Combined CoT & ToT methodology – use and application
- Sequential prompting
- Agents in M&A:
- Emerging applications
- Key limitations
Session 3: M&A-Specific Prompting Case Studies & Worked Examples
This session demonstrates advanced AI in M&A prompting techniques through four comprehensive case studies drawn from live M&A transactions. Rather than theoretical exercises, each example walks through the complete methodology progression from initial prompt development to sophisticated output refinement.
Each case study follows a structured demonstration sequence: we begin with the commercial challenge and stakeholder dynamics, then observe the systematic application of the SCOPE framework to develop targeted initial prompts. The core demonstration involves live Chain-of-Thought and Tree-of-Thought methodologies, showing how sequential prompt refinement transforms basic outputs into genuinely useful professional work product.
Case Studies
Case Study 1: Valuation (Trading Comparables Analysis)
Case Snapshot: A corporate finance director at an investment bank advising on SaaS business valuation, requiring comprehensive trading comparables analysis to establish a credible valuation range for a £500M technology acquisition. Client needs a systematic approach to identify truly comparable companies, filter out inappropriate matches, and derive defensible multiple ranges that will withstand buyer scrutiny during negotiations.
AI Methodology Walkthrough: Using the SCOPE framework to develop initial prompts, then a step-by-step demonstration of how Chain-of-Thought methodology systematically identifies relevant peer companies, filters based on business model similarities (recurring revenue, growth rates, geographic focus), and calculates meaningful multiple ranges. Sequential prompting refinement shows progression from basic comparable identification to sophisticated analysis incorporating size adjustments, growth differentials, and market positioning factors that enable credible valuation positioning.
Case study 2. Negotiation Strategy Development
Case Snapshot: Investment banker advising on acquisition of German automotive supplier requiring sophisticated deal positioning strategy that accounts for multiple bidder dynamics, synergy assumptions, and stakeholder priorities. Client needs a tactical negotiation framework that can adapt to changing bid dynamics whilst maintaining a credible pricing rationale and preserving negotiating leverage throughout the auction process.
AI Methodology Walkthrough: Using SCOPE framework to develop initial prompts, then step-by-step demonstration of how Chain-of-Thought methodology systematically analyses seller motivations (CEO seeking maximum value, CTO prioritising cultural fit, investors wanting certainty), develops tiered pricing strategies with supporting rationale, and applies Tree-of-Thought planning to anticipate competitor responses and maintain tactical flexibility when uncertainties arose regarding projected NPV savings from operational synergies, forcing comprehensive re-evaluation of bid positioning and price justification during critical mid-auction phase.
Case study 3: Earn-outs (Earn-Out Structure Optimisation for Multi-Seller Scenarios)
Case Snapshot: Senior M&A lawyer advising MediaGroup PLC (listed strategic acquirer) on competitive auction requiring differentiated earn-out proposal targeting 3 founding partners with different exit preferences and risk appetites. Client requires a sophisticated earn-out structure, differentiating their offer from standard approaches in a hotly contested process.
AI Methodology Walkthrough: Using the SCOPE framework to develop initial prompts, then a step-by-step demonstration of how Chain-of-Thought methodology systematically analyses individual seller preferences (CEO wants maximum upside, CTO seeks balance, investor wants certainty), followed by Tree-of-Thought planning that enables dynamic structure adjustment when rival bidders modify their offers mid-auction.
Case study 4. Locked Box – leakage analysis
Case Snapshot: Senior M&A lawyer advising TechCorp Ltd (strategic acquirer) on locked box mechanism for £250m acquisition of family-owned manufacturing business requiring comprehensive leakage framework during 8-month completion timeline. Complex intercompany trading arrangements with retained family companies (£25m annual sales, £15m property rental) create sophisticated value transfer risks requiring careful classification and monitoring.
AI Methodology Walkthrough: Using the SCOPE framework to develop initial prompts, then a step-by-step demonstration of how the Chain-of-Thought methodology systematically classifies prohibited vs conditional vs notification leakage categories (family dividends, intercompany pricing, transfer arrangements) followed by Tree-of-Thought planning that enables dynamic response to pricing disputes and volume changes during an extended period.